From 7cd2770d8de1ec8d5bdbee61d4f3272dfe48dc51 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=9C=D0=B0=D0=BA=D1=81=D0=B8=D0=BC?= Date: Sat, 17 May 2025 08:07:42 +0300 Subject: [PATCH] =?UTF-8?q?=D0=9F=D1=80=D0=BE=D0=B5=D0=BA=D1=82=20=D0=B3?= =?UTF-8?q?=D0=BE=D1=82=D0=BE=D0=B2?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .gitignore | 4 +- Untitled.ipynb | 309 ++++++++++++++++++++++++++++++++++++ Untitled1.ipynb | 237 +++++++++++++++++++++++++++ plot_sgd_comparison copy.py | 108 +++++++++++++ plot_sgd_comparison.py | 70 ++++++++ 5 files changed, 727 insertions(+), 1 deletion(-) create mode 100644 Untitled.ipynb create mode 100644 Untitled1.ipynb create mode 100644 plot_sgd_comparison copy.py create mode 100644 plot_sgd_comparison.py diff --git a/.gitignore b/.gitignore index 0cafc1c..96f3402 100644 --- a/.gitignore +++ b/.gitignore @@ -1 +1,3 @@ -.venv/ \ No newline at end of file +.venv/ +data +.ipynb_checkpoints/ \ No newline at end of file diff --git a/Untitled.ipynb b/Untitled.ipynb new file mode 100644 index 0000000..edb8d2c --- /dev/null +++ b/Untitled.ipynb @@ -0,0 +1,309 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c42ce76a-1533-4957-8eed-cfe46f9782d8", + "metadata": {}, + "source": [ + "Цель: \n", + "\n", + "Сравнить эффективность различных онлайн-алгоритмов машинного обучения при решении задачи распознавания рукописных букв из датасета EMNIST Letters . \n", + "Задача: \n", + "\n", + " Распознавание рукописных латинских букв (A–Z).\n", + " Классификация изображений размером 28×28 пикселей.\n", + " Сравнение алгоритмов при разных пропорциях обучающей и тестовой выборок.\n", + " \n", + "\n", + "Используемые алгоритмы: \n", + "\n", + " SGDClassifier — стохастический градиентный спуск\n", + " Perceptron\n", + " PassiveAggressiveClassifier I/II\n", + " LogisticRegression с SAG-оптимизацией\n", + " \n", + "\n", + "Источник данных: \n", + "\n", + " Датасет: EMNIST Letters \n", + " Образцы: 145 600 изображений рукописных букв A–Z\n", + " В работе используется ограниченная подвыборка (~1800 образцов) для сравнения с digits() из sklearn.\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "cdf3670c-57c6-4201-9f60-49f024da6ac5", + "metadata": {}, + "source": [ + "Препроцессинг данных" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "6fb3efcb-1318-4960-92da-7cc65da23462", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Форма данных: (1800, 784) (1800,)\n" + ] + } + ], + "source": [ + "import torch\n", + "from torchvision import datasets, transforms\n", + "import numpy as np\n", + "\n", + "# Преобразование изображений в одномерные массивы\n", + "transform = transforms.Compose([\n", + " transforms.ToTensor(),\n", + " lambda x: x.view(-1).numpy()\n", + "])\n", + "\n", + "# Загрузка EMNIST\n", + "train_dataset = datasets.EMNIST(\n", + " root='./data', split='letters', train=True, download=True, transform=transform\n", + ")\n", + "test_dataset = datasets.EMNIST(\n", + " root='./data', split='letters', train=False, download=True, transform=transform\n", + ")\n", + "\n", + "# Извлечение данных\n", + "X_train = [x for x, y in train_dataset]\n", + "y_train = [y - 1 for x, y in train_dataset] # Перенумерация меток: 1..26 → 0..25\n", + "\n", + "X_test = [x for x, y in test_dataset]\n", + "y_test = [y - 1 for x, y in test_dataset]\n", + "\n", + "# Объединение выборок\n", + "X = np.array(X_train + X_test)\n", + "y = np.array(y_train + y_test)\n", + "\n", + "# Ограничение до ~1800 образцов\n", + "SAMPLE_LIMIT = 1800\n", + "X = X[:SAMPLE_LIMIT]\n", + "y = y[:SAMPLE_LIMIT]\n", + "\n", + "print(\"Форма данных:\", X.shape, y.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "7a413e1f-a89e-448c-aa15-07b6fcc6565b", + "metadata": {}, + "source": [ + "Обучение модели" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "496c57eb-04c6-45ac-a8ca-1ca84dcf6db4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Обучение: SGD\n", + "Обучение: ASGD\n", + "Обучение: Perceptron\n", + "Обучение: Passive-Aggressive I\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/max/lab4/.venv/lib/python3.12/site-packages/sklearn/linear_model/_stochastic_gradient.py:738: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Обучение: Passive-Aggressive II\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/max/lab4/.venv/lib/python3.12/site-packages/sklearn/linear_model/_stochastic_gradient.py:738: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n", + "/home/max/lab4/.venv/lib/python3.12/site-packages/sklearn/linear_model/_stochastic_gradient.py:738: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Обучение: SAG\n" + ] + } + ], + "source": [ + "from sklearn.linear_model import (\n", + " LogisticRegression,\n", + " PassiveAggressiveClassifier,\n", + " Perceptron,\n", + " SGDClassifier,\n", + ")\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# Параметры эксперимента\n", + "heldout = [0.95, 0.90, 0.75, 0.50, 0.01] # Доли тестовой выборки\n", + "rounds = 10 # Число повторений для усреднения\n", + "rng = np.random.RandomState(42)\n", + "\n", + "# Модели\n", + "classifiers = [\n", + " (\"SGD\", SGDClassifier(max_iter=110)),\n", + " (\"ASGD\", SGDClassifier(max_iter=110, average=True)),\n", + " (\"Perceptron\", Perceptron(max_iter=110)),\n", + " (\"Passive-Aggressive I\", PassiveAggressiveClassifier(max_iter=110, loss=\"hinge\", C=1.0, tol=1e-4)),\n", + " (\"Passive-Aggressive II\", PassiveAggressiveClassifier(max_iter=110, loss=\"squared_hinge\", C=1.0, tol=1e-4)),\n", + " (\"SAG\", LogisticRegression(max_iter=110, solver=\"sag\", tol=1e-1, C=1.0e4 / X.shape[0])),\n", + "]\n", + "\n", + "xx = 1.0 - np.array(heldout) # Пропорция обучающих данных\n", + "\n", + "# Обучение и оценка моделей\n", + "results = {}\n", + "\n", + "for name, clf in classifiers:\n", + " print(f\"Обучение: {name}\")\n", + " yy = []\n", + " for test_size in heldout:\n", + " errors = []\n", + " for r in range(rounds):\n", + " X_train_part, X_test_part, y_train_part, y_test_part = train_test_split(\n", + " X, y, test_size=test_size, random_state=rng\n", + " )\n", + " clf.fit(X_train_part, y_train_part)\n", + " y_pred = clf.predict(X_test_part)\n", + " error_rate = 1 - np.mean(y_pred == y_test_part)\n", + " errors.append(error_rate)\n", + " yy.append(np.mean(errors))\n", + " results[name] = yy" + ] + }, + { + "cell_type": "markdown", + "id": "dcef6843-cd27-439a-8d58-db11164ff85c", + "metadata": {}, + "source": [ + "Визуализация" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c8ed30cb-505b-4a23-9778-4e095ddafc3c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "\n", + "for name, values in results.items():\n", + " plt.plot(xx, values, label=name)\n", + "\n", + "plt.legend(loc=\"upper right\")\n", + "plt.xlabel(\"Пропорция обучающей выборки\")\n", + "plt.ylabel(\"Ошибка на тесте\")\n", + "plt.title(\"Сравнение онлайн-алгоритмов на уменьшенном EMNIST Letters\")\n", + "plt.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "ac0cf458-11f5-402c-955f-99b5124abd16", + "metadata": {}, + "source": [ + "Интерпретация результатов \n", + "\n", + "На графике видно, как ошибка классификации меняется в зависимости от доли обучающих данных , для каждого из используемых алгоритмов. \n", + "Что можно заметить: \n", + "\n", + " SGDClassifier может показывать хорошие результаты при больших наборах данных, но менее стабильный при малых.\n", + " ASGD (усреднённый SGD) часто более стабилен и быстрее сходится.\n", + " PassiveAggressive I/II хорошо подходят для потокового обучения и реагируют только на ошибки.\n", + " SAG (LogisticRegression с SAG) может быть наиболее точным при достаточном объёме данных.\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "748cf616-eb27-4be3-adfb-90d1a785d2bd", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "88a7ccea-cded-4a75-acb2-b7eade0f1d92", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "22c1549d-fff3-4355-81a4-03c84b4f505a", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "97d9aefd-1ff6-41d2-836d-aa0c2b54e9ec", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Untitled1.ipynb b/Untitled1.ipynb new file mode 100644 index 0000000..542b29b --- /dev/null +++ b/Untitled1.ipynb @@ -0,0 +1,237 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "13783f38-056a-4ec7-aaff-fa6c6bc85761", + "metadata": {}, + "source": [ + "Цель: \n", + "\n", + "Продемонстрировать, как различные онлайн-алгоритмы машинного обучения работают на наборе данных digits из библиотеки scikit-learn. \n", + "Задача: \n", + "\n", + " Классификация рукописных цифр (0–9).\n", + " Сравнение алгоритмов при разных долях обучающей/тестовой выборок.\n", + " Построение графика зависимости ошибки от размера обучающей выборки.\n", + " \n", + "\n", + "Используемые алгоритмы: \n", + "\n", + " SGDClassifier\n", + " ASGDClassifier (усреднённый SGD)\n", + " Perceptron\n", + " PassiveAggressiveClassifier I / II\n", + " LogisticRegression с SAG-оптимизатором\n", + " \n", + "\n", + "Источник данных: \n", + "\n", + " Датасет: sklearn.datasets.load_digits() — содержит ~1800 изображений рукописных цифр (размер каждого изображения — 8x8 пикселей).\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "e9025640-70d9-4d05-af1b-6e4001a2580b", + "metadata": {}, + "source": [ + "Препроцессинг данных" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c58677a8-2837-4de8-847f-9e9071b3f112", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Форма данных: (1797, 64)\n", + "Количество классов: 10\n" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "from sklearn import datasets\n", + "from sklearn.linear_model import (\n", + " LogisticRegression,\n", + " PassiveAggressiveClassifier,\n", + " Perceptron,\n", + " SGDClassifier,\n", + ")\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# Доли тестовой выборки\n", + "heldout = [0.95, 0.90, 0.75, 0.50, 0.01]\n", + "\n", + "# Число повторений для усреднения\n", + "rounds = 10\n", + "\n", + "# Загрузка данных\n", + "X, y = datasets.load_digits(return_X_y=True)\n", + "\n", + "print(\"Форма данных:\", X.shape)\n", + "print(\"Количество классов:\", len(np.unique(y)))" + ] + }, + { + "cell_type": "markdown", + "id": "3eb2a945-6e1e-49a0-b6b2-84ef3c7eba4c", + "metadata": {}, + "source": [ + "Обучение модели" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8fff5c5c-9a15-496b-9ad4-da8a6eb7a31a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Обучение модели: SGD\n", + "Обучение модели: ASGD\n", + "Обучение модели: Perceptron\n", + "Обучение модели: Passive-Aggressive I\n", + "Обучение модели: Passive-Aggressive II\n", + "Обучение модели: SAG\n" + ] + } + ], + "source": [ + "# Список моделей для сравнения\n", + "classifiers = [\n", + " (\"SGD\", SGDClassifier(max_iter=110)),\n", + " (\"ASGD\", SGDClassifier(max_iter=110, average=True)),\n", + " (\"Perceptron\", Perceptron(max_iter=110)),\n", + " (\"Passive-Aggressive I\", PassiveAggressiveClassifier(\n", + " max_iter=110, loss=\"hinge\", C=1.0, tol=1e-4)),\n", + " (\"Passive-Aggressive II\", PassiveAggressiveClassifier(\n", + " max_iter=110, loss=\"squared_hinge\", C=1.0, tol=1e-4)),\n", + " (\"SAG\", LogisticRegression(\n", + " max_iter=110, solver=\"sag\", tol=1e-1, C=1.0e4 / X.shape[0])),\n", + "]\n", + "\n", + "xx = 1.0 - np.array(heldout) # пропорции обучающей выборки\n", + "\n", + "results = {}\n", + "\n", + "# Цикл по всем моделям\n", + "for name, clf in classifiers:\n", + " print(f\"Обучение модели: {name}\")\n", + " yy = []\n", + " for i in heldout:\n", + " errors = []\n", + " for r in range(rounds):\n", + " # Разделение выборки\n", + " X_train, X_test, y_train, y_test = train_test_split(\n", + " X, y, test_size=i, random_state=np.random.RandomState(42)\n", + " )\n", + " # Обучение\n", + " clf.fit(X_train, y_train)\n", + " # Предсказание\n", + " y_pred = clf.predict(X_test)\n", + " # Вычисление ошибки\n", + " error_rate = 1 - np.mean(y_pred == y_test)\n", + " errors.append(error_rate)\n", + " yy.append(np.mean(errors))\n", + " results[name] = yy" + ] + }, + { + "cell_type": "markdown", + "id": "daf68ee8-d0dc-496d-82d9-f65e2b94db7c", + "metadata": {}, + "source": [ + "Визуализация результатов" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e77a5e48-942e-4125-9706-cec8575cfa61", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 6))\n", + "\n", + "for name, values in results.items():\n", + " plt.plot(xx, values, label=name)\n", + "\n", + "plt.legend(loc=\"upper right\")\n", + "plt.xlabel(\"Доля обучающей выборки\")\n", + "plt.ylabel(\"Ошибка на тесте\")\n", + "plt.title(\"Сравнение онлайн-алгоритмов на наборе данных digits\")\n", + "plt.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c6d3045d-346b-4dbd-a907-48a3cc2cc965", + "metadata": {}, + "source": [ + "Интерпретация результатов \n", + "\n", + "На графике показана зависимость ошибки на тестовой выборке от доли обучающих данных для каждой модели. \n", + "Что можно заметить: \n", + "\n", + " SGDClassifier : работает хорошо при большом количестве данных, но менее стабильно при малых объёмах.\n", + " ASGDClassifier : более стабильный за счёт усреднения весов.\n", + " Perceptron : простой и быстрый, но может не достигать высокой точности.\n", + " PassiveAggressive I/II : реагируют только на ошибки, хорошо подходят для потокового обучения.\n", + " SAG (логистическая регрессия) : показывает хорошую точность при достаточном объёме данных.\n", + " \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "16f05f88-1155-412c-9b66-68b5b04836e8", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/plot_sgd_comparison copy.py b/plot_sgd_comparison copy.py new file mode 100644 index 0000000..d7c96af --- /dev/null +++ b/plot_sgd_comparison copy.py @@ -0,0 +1,108 @@ +import torch +from torchvision import datasets, transforms +import numpy as np +import matplotlib.pyplot as plt + +from sklearn.linear_model import ( + LogisticRegression, + PassiveAggressiveClassifier, + Perceptron, + SGDClassifier, +) +from sklearn.model_selection import train_test_split + +# ----------------------------- +# 1. Загрузка и подготовка данных +# ----------------------------- + +transform = transforms.Compose([ + transforms.ToTensor(), # Преобразуем в тензор + lambda x: x.view(-1).numpy() # Преобразуем изображение в одномерный массив +]) + +# Загружаем EMNIST (Letters): содержит буквы A-Z +train_dataset = datasets.EMNIST( + root='./data', split='letters', train=True, download=True, transform=transform +) +test_dataset = datasets.EMNIST( + root='./data', split='letters', train=False, download=True, transform=transform +) + +# Объединяем train и test +X_train = [x for x, y in train_dataset] +y_train = [y - 1 for x, y in train_dataset] # метки от 1 до 26 -> делаем 0..25 + +X_test = [x for x, y in test_dataset] +y_test = [y - 1 for x, y in test_dataset] + +# Объединяем всё в один набор +X = np.array(X_train + X_test) +y = np.array(y_train + y_test) + +# 🔍 Ограничиваем данные до ~1800 образцов (как в digits()) +SAMPLE_LIMIT = 1800 +X = X[:SAMPLE_LIMIT] +y = y[:SAMPLE_LIMIT] + +print("Данные загружены:", X.shape, y.shape) + +# ----------------------------- +# 2. Настройка моделей +# ----------------------------- + +heldout = [0.95, 0.90, 0.75, 0.50, 0.01] # доли тестовой выборки +rounds = 10 # число повторений для усреднения + +classifiers = [ + ("SGD", SGDClassifier(max_iter=110)), + ("ASGD", SGDClassifier(max_iter=110, average=True)), + ("Perceptron", Perceptron(max_iter=110)), + ( + "Passive-Aggressive I", + PassiveAggressiveClassifier(max_iter=110, loss="hinge", C=1.0, tol=1e-4), + ), + ( + "Passive-Aggressive II", + PassiveAggressiveClassifier( + max_iter=110, loss="squared_hinge", C=1.0, tol=1e-4 + ), + ), + ( + "SAG", + LogisticRegression(max_iter=110, solver="sag", tol=1e-1, C=1.0e4 / X.shape[0]), + ), +] + +xx = 1.0 - np.array(heldout) # пропорция обучающей выборки + +# ----------------------------- +# 3. Обучение и оценка моделей +# ----------------------------- + +for name, clf in classifiers: + print(f"Обучение: {name}") + rng = np.random.RandomState(42) + yy = [] + for test_size in heldout: + errors = [] + for r in range(rounds): + X_train_part, X_test_part, y_train_part, y_test_part = train_test_split( + X, y, test_size=test_size, random_state=rng + ) + clf.fit(X_train_part, y_train_part) + y_pred = clf.predict(X_test_part) + error_rate = 1 - np.mean(y_pred == y_test_part) + errors.append(error_rate) + yy.append(np.mean(errors)) + plt.plot(xx, yy, label=name) + +# ----------------------------- +# 4. Визуализация результатов +# ----------------------------- + +plt.legend(loc="upper right") +plt.xlabel("Пропорция обучающей выборки") +plt.ylabel("Ошибка на тесте") +plt.title("Сравнение онлайн-алгоритмов на уменьшенном EMNIST Letters") +plt.grid(True) +plt.show() \ No newline at end of file diff --git a/plot_sgd_comparison.py b/plot_sgd_comparison.py new file mode 100644 index 0000000..c24ad14 --- /dev/null +++ b/plot_sgd_comparison.py @@ -0,0 +1,70 @@ +""" +================================== +Comparing various online solvers +================================== +An example showing how different online solvers perform +on the hand-written digits dataset. +""" + +# Authors: The scikit-learn developers +# SPDX-License-Identifier: BSD-3-Clause + +import matplotlib.pyplot as plt +import numpy as np + +from sklearn import datasets +from sklearn.linear_model import ( + LogisticRegression, + PassiveAggressiveClassifier, + Perceptron, + SGDClassifier, +) +from sklearn.model_selection import train_test_split + +heldout = [0.95, 0.90, 0.75, 0.50, 0.01] +# Number of rounds to fit and evaluate an estimator. +rounds = 10 +X, y = datasets.load_digits(return_X_y=True) + +classifiers = [ + ("SGD", SGDClassifier(max_iter=110)), + ("ASGD", SGDClassifier(max_iter=110, average=True)), + ("Perceptron", Perceptron(max_iter=110)), + ( + "Passive-Aggressive I", + PassiveAggressiveClassifier(max_iter=110, loss="hinge", C=1.0, tol=1e-4), + ), + ( + "Passive-Aggressive II", + PassiveAggressiveClassifier( + max_iter=110, loss="squared_hinge", C=1.0, tol=1e-4 + ), + ), + ( + "SAG", + LogisticRegression(max_iter=110, solver="sag", tol=1e-1, C=1.0e4 / X.shape[0]), + ), +] + +xx = 1.0 - np.array(heldout) + +for name, clf in classifiers: + print("training %s" % name) + rng = np.random.RandomState(42) + yy = [] + for i in heldout: + yy_ = [] + for r in range(rounds): + X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size=i, random_state=rng + ) + clf.fit(X_train, y_train) + y_pred = clf.predict(X_test) + yy_.append(1 - np.mean(y_pred == y_test)) + yy.append(np.mean(yy_)) + plt.plot(xx, yy, label=name) + +plt.legend(loc="upper right") +plt.xlabel("Proportion train") +plt.ylabel("Test Error Rate") +plt.show()